"""
# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""

import argparse
import concurrent.futures
import gc
import json
import queue
import threading
import time
import traceback
from typing import List

import numpy as np
import paddle

from fastdeploy import envs
from fastdeploy.cache_manager.cache_data import CacheStatus
from fastdeploy.cache_manager.ops import (
    cuda_host_alloc,
    cuda_host_free,
    memory_allocated,
    set_data_ipc,
    set_device,
    share_external_data_,
    swap_cache_all_layers,
    swap_cache_layout,
    unset_data_ipc,
)
from fastdeploy.cache_manager.transfer_factory import MooncakeStore
from fastdeploy.config import SpeculativeConfig
from fastdeploy.inter_communicator import EngineCacheQueue, IPCSignal, KVCacheStatus
from fastdeploy.platforms import current_platform
from fastdeploy.utils import get_logger


def parse_args():
    """
    从命令行解析参数
    """
    parser = argparse.ArgumentParser("Cache transfer manager")
    parser.add_argument(
        "--splitwise_role",
        type=str,
        default="mixed",
        help="splitwise role, can be decode, prefill or mixed",
    )
    parser.add_argument("--rank", type=int, default=0, help="local tp rank")
    parser.add_argument("--device_id", type=int, default=0, help="device id")
    parser.add_argument("--max_model_len", type=int, default=32768, help="max model length")
    parser.add_argument("--num_layers", type=int, default=1, help="model num layers")
    parser.add_argument("--mp_num", type=int, default=1, help="number of model parallel")
    parser.add_argument(
        "--cache_dtype",
        type=str,
        default="bfloat16",
        choices=["uint8", "bfloat16", "block_wise_fp8"],
        help="cache dtype",
    )
    parser.add_argument(
        "--default_dtype",
        type=str,
        default="bfloat16",
        choices=["float16", "bfloat16", "uint8"],
        help="paddle default dtype, swap_cache_batch only support float16、bfloat16 and uint8 now",
    )
    parser.add_argument("--key_cache_shape", type=str, default="", help="key cache shape")
    parser.add_argument("--value_cache_shape", type=str, default="", help="value cache shape")
    parser.add_argument("--cache_queue_port", type=int, default=9923, help="cache queue port")
    parser.add_argument("--enable_splitwise", type=int, default=0, help="enable splitwise ")
    parser.add_argument("--pod_ip", type=str, default="0.0.0.0", help="pod ip")
    parser.add_argument(
        "--engine_worker_queue_port",
        type=int,
        default=9923,
        help="engine worker queue port",
    )
    parser.add_argument("--num_cpu_blocks", type=int, default=4, help="cpu cache block number")
    parser.add_argument("--ipc_suffix", type=str, default=None, help="engine pid")
    parser.add_argument(
        "--protocol",
        type=str,
        default="ipc",
        help="cache transfer protocol, only support ipc now",
    )
    parser.add_argument("--local_data_parallel_id", type=int, default=0)
    parser.add_argument("--rdma_port", type=str, default="", help="rmda port")
    parser.add_argument(
        "--speculative_config",
        type=json.loads,
        default="{}",
        help="speculative config",
    )
    parser.add_argument("--create_cache_tensor", action="store_true")
    parser.add_argument(
        "--kvcache_storage_backend",
        type=str,
        default=None,
        choices=["mooncake", "none"],
        help="The storage backend for kvcache storage. If not set, storage backend is disabled.",
    )
    parser.add_argument(
        "--write_policy",
        type=str,
        choices=["write_through"],
        default="write_through",
        help="KVCache write policy",
    )

    args = parser.parse_args()
    return args


class CacheTransferManager:
    """
    管理CPU和GPU之间缓存的交换传输
    """

    def __init__(self, args):
        """
        初始化CacheTransferManager
        """
        device = args.device_id
        rank = args.rank
        self.gpu_cache_kvs = {}
        self.cpu_cache_kvs = {}
        self.gpu_cache_k_tensors = []
        self.gpu_cache_v_tensors = []
        self.gpu_cache_scales_k_tensors = []
        self.gpu_cache_scales_v_tensors = []
        self.speculative_config = SpeculativeConfig(args.speculative_config)
        self.key_cache_shape = [int(i) for i in args.key_cache_shape.split(",")]
        self.value_cache_shape = []
        if args.value_cache_shape:
            self.value_cache_shape = [int(i) for i in args.value_cache_shape.split(",")]
        self.num_gpu_blocks = self.key_cache_shape[0]
        self.num_extra_layers = self.speculative_config.num_extra_cache_layer
        self.num_extra_layer_gpu_blocks = int(self.num_gpu_blocks * self.speculative_config.num_gpu_block_expand_ratio)
        paddle.set_default_dtype(args.default_dtype)

        self.swap_to_cpu_thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=1)
        self.swap_to_gpu_thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=1)
        self.read_storage_thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=1)
        self.write_back_storage_thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=1)
        self.timeout_thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=2)
        self.transfer_task_queue = queue.Queue()  # 用来接收传输任务
        self.tansfer_done_queue = queue.Queue()  # 用来告知任务执行完毕
        self.n_ranks = args.mp_num
        self.rank = rank
        self.device = device
        self.ipc_suffix = args.ipc_suffix
        self.cache_dtype = args.cache_dtype

        address = (args.pod_ip, args.cache_queue_port)
        self.cache_task_queue = EngineCacheQueue(
            address=address,
            is_server=False,
            num_client=args.mp_num,
            client_id=rank,
            local_data_parallel_id=args.local_data_parallel_id,
        )

        cache_ready_signal_data = np.zeros(shape=[args.mp_num], dtype=np.int32)
        self.cache_ready_signal = IPCSignal(
            name="cache_ready_signal",
            array=cache_ready_signal_data,
            dtype=np.int32,
            suffix=self.ipc_suffix,
            create=False,
        )
        swap_space_ready_data = np.zeros(shape=[args.mp_num], dtype=np.int32)
        self.swap_space_ready_signal = IPCSignal(
            name="swap_space_ready_signal",
            array=swap_space_ready_data,
            dtype=np.int32,
            suffix=self.ipc_suffix,
            create=False,
        )

        self.num_cpu_blocks = args.num_cpu_blocks

        self._init_gpu_cache(args)
        if self.num_cpu_blocks > 0:
            self._init_cpu_cache(args)

        cache_task_broadcast_data = np.zeros(shape=[1], dtype=np.int32)
        self.cache_task_broadcast_signal = IPCSignal(
            name="cache_task_broadcast_signal",
            array=cache_task_broadcast_data,
            dtype=np.int32,
            suffix=args.ipc_suffix,
            create=False,
        )

        max_chips_per_node = 16 if current_platform.is_iluvatar() else 8
        array_size = min(max_chips_per_node, args.mp_num)
        worker_healthy_live_array = np.zeros(shape=[array_size], dtype=np.int32)
        self.worker_healthy_live_signal = IPCSignal(
            name="worker_healthy_live_signal",
            array=worker_healthy_live_array,
            dtype=np.int32,
            suffix=args.engine_worker_queue_port,
            create=False,
        )

        if args.kvcache_storage_backend is None or args.kvcache_storage_backend == "none":
            self.storage_backend = None
        elif args.kvcache_storage_backend == "mooncake":
            logger.info("Start initialize mooncake store...")
            self.storage_backend = MooncakeStore(tp_rank=self.rank)
            self._init_storage_buffer(args)
            logger.info("Initialized mooncake store successfully")
        else:
            raise NotImplementedError(f"Unsupported storage backend: {args.kvcache_storage_backend}")

        if args.write_policy not in ["write_through"]:
            raise ValueError(f"Invalid write policy: {args.write_policy}")
        self.write_policy = args.write_policy

        threading.Thread(target=self.clear_or_update_caches, args=[args], daemon=True).start()

    def _init_storage_buffer(self, args):
        """
        Initialize pinned memory buffer that can hold the cache for a longest request
        cache layout: layer_num * [block_num, head_num, block_size, head_dim]
        buffer layout: [block_num, layer_num, head_num, block_size, head_dim]
        """
        layer_num = args.num_layers + self.num_extra_layers
        head_num = self.key_cache_shape[1]
        block_size = self.key_cache_shape[2]
        head_dim = self.key_cache_shape[3]
        block_num = (args.max_model_len + block_size - 1) // block_size
        logger.info(
            f"Creating cache buffer for storage with shape: "
            f"[{block_num}, {layer_num}, {head_num}, {block_size}, {head_dim}]"
        )

        self.cache_bytes = self._get_cache_bytes(self.cache_dtype)
        self.storage_buffer_stride_bytes = layer_num * head_num * block_size * head_dim * self.cache_bytes
        total_bytes = block_num * self.storage_buffer_stride_bytes * 2  # key and value

        logger.info(f"Creating cpu buffer cache for alllayers: {total_bytes / 1024 ** 3:.2f}GB")
        read_buffer = cuda_host_alloc(total_bytes)
        self.storage_key_read_buffer = read_buffer
        self.storage_value_read_buffer = read_buffer + total_bytes // 2
        self.storage_backend.register_buffer(read_buffer, total_bytes)

        write_buffer = cuda_host_alloc(total_bytes)
        self.storage_key_write_buffer = write_buffer
        self.storage_value_write_buffer = write_buffer + total_bytes // 2
        self.storage_backend.register_buffer(write_buffer, total_bytes)

    def _init_gpu_cache(self, args):

        try:
            assert not args.create_cache_tensor
        except:
            logger.warn(
                f"In current implementation, cache transfer manager do not create cache tensors at all, "
                f"meaning create_cache_tensor should be False, while we got {args.create_cache_tensor}. "
                f"Cache tensor creation will occur in: 1) model runner in case of mixed deployment; "
                f"or 2) cache messager in case of disaggregation deployment. "
                f"Please check the codes and make sure they work correctly."
            )
        if not args.create_cache_tensor:
            logger.info(f"[rank {self.rank}/{self.n_ranks}] Waiting for runners or messagers to create kv cache.")
            while self.cache_ready_signal.value[self.rank] != 1:
                time.sleep(0.1)
            logger.info(f"[rank {self.rank}/{self.n_ranks}] OK! Stop waiting.")

        if args.cache_dtype == "block_wise_fp8":
            cache_type = "uint8"
        else:
            cache_type = args.cache_dtype

        logger.info(f"[rank {self.rank}/{self.n_ranks}] Initializing kv cache for all layers.")
        set_device(self.device)
        for i in range(args.num_layers + self.num_extra_layers):
            num_gpu_blocks = self.num_gpu_blocks if i < args.num_layers else self.num_extra_layer_gpu_blocks
            key_name = f"key_caches_{i}_rank{self.rank}.device{self.device}"
            val_name = f"value_caches_{i}_rank{self.rank}.device{self.device}"
            key_cache_scales_name = f"key_cache_scales_{i}_rank{self.rank}.device{self.device}"
            value_cache_scales_name = f"value_cache_scales_{i}_rank{self.rank}.device{self.device}"
            key_cache_shape = [
                num_gpu_blocks,
                self.key_cache_shape[1],
                self.key_cache_shape[2],
                self.key_cache_shape[3],
            ]
            value_cache_shape = []
            if self.value_cache_shape:
                value_cache_shape = [
                    num_gpu_blocks,
                    self.value_cache_shape[1],
                    self.value_cache_shape[2],
                    self.value_cache_shape[3],
                ]
            if args.create_cache_tensor:
                logger.info(
                    f"[rank {self.rank}/{self.n_ranks}] ..creating kv cache for layer {i}: {key_cache_shape} {value_cache_shape}"
                )
                key_cache = paddle.full(shape=key_cache_shape, fill_value=0, dtype=cache_type)
                set_data_ipc(key_cache, key_name)

                if args.cache_dtype == "block_wise_fp8":
                    key_cache_scales = paddle.full(
                        shape=[num_gpu_blocks, self.key_cache_shape[1], self.key_cache_shape[2]],
                        fill_value=0,
                        dtype=paddle.get_default_dtype(),
                    )
                    set_data_ipc(key_cache_scales, key_cache_scales_name)
                if self.value_cache_shape:
                    val_cache = paddle.full(shape=value_cache_shape, fill_value=0, dtype=cache_type)
                    set_data_ipc(val_cache, val_name)

                    if args.cache_dtype == "block_wise_fp8":
                        value_cache_scales = paddle.full(
                            shape=[num_gpu_blocks, self.value_cache_shape[1], self.value_cache_shape[2]],
                            fill_value=0,
                            dtype=paddle.get_default_dtype(),
                        )
                        set_data_ipc(value_cache_scales, value_cache_scales_name)
            else:
                logger.info(
                    f"[rank {self.rank}/{self.n_ranks}] ..attaching kv cache for layer {i}: {key_cache_shape} {value_cache_shape}"
                )
                key_cache = paddle.empty(shape=[], dtype=cache_type)
                val_cache = paddle.empty(shape=[], dtype=cache_type)
                key_cache = share_external_data_(key_cache, key_name, key_cache_shape, True)
                if args.cache_dtype == "block_wise_fp8":
                    key_cache_scales = paddle.empty(shape=[], dtype=paddle.get_default_dtype())
                    key_cache_scales = share_external_data_(
                        key_cache_scales,
                        key_cache_scales_name,
                        [num_gpu_blocks, self.key_cache_shape[1], self.key_cache_shape[2]],
                        True,
                    )
                if self.value_cache_shape:
                    val_cache = share_external_data_(val_cache, val_name, value_cache_shape, True)
                    if args.cache_dtype == "block_wise_fp8":
                        value_cache_scales = paddle.empty(shape=[], dtype=paddle.get_default_dtype())
                        value_cache_scales = share_external_data_(
                            value_cache_scales,
                            value_cache_scales_name,
                            [num_gpu_blocks, self.value_cache_shape[1], self.value_cache_shape[2]],
                            True,
                        )

            self.gpu_cache_kvs[key_name] = key_cache
            self.gpu_cache_k_tensors.append(self.gpu_cache_kvs[key_name])
            if args.cache_dtype == "block_wise_fp8":
                self.gpu_cache_kvs[key_cache_scales_name] = key_cache_scales
                self.gpu_cache_scales_k_tensors.append(self.gpu_cache_kvs[key_cache_scales_name])
            if args.value_cache_shape:
                self.gpu_cache_kvs[val_name] = val_cache
                self.gpu_cache_v_tensors.append(self.gpu_cache_kvs[val_name])
                if args.cache_dtype == "block_wise_fp8":
                    self.gpu_cache_kvs[value_cache_scales_name] = value_cache_scales
                    self.gpu_cache_scales_v_tensors.append(self.gpu_cache_kvs[value_cache_scales_name])

        if args.create_cache_tensor:
            logger.info(f"[rank {self.rank}/{self.n_ranks}] ✅ kv cache is ready!")
            self.cache_ready_signal.value[self.rank] = 1

        cache_kv_size_byte = sum([tmp.numel() * 1 for key, tmp in self.gpu_cache_kvs.items()])
        logger.info(f"[rank {self.rank}/{self.n_ranks}] device :{self.device}")
        logger.info(f"[rank {self.rank}/{self.n_ranks}] cache_kv_size_byte : {cache_kv_size_byte}")
        logger.info(f"[rank {self.rank}/{self.n_ranks}] done init cache (full) gmem alloc : {memory_allocated()}")

    def _init_cpu_cache(self, args):
        key_cache_size = self.key_cache_shape[1] * self.key_cache_shape[2] * self.key_cache_shape[3]
        if args.value_cache_shape:
            value_cache_size = self.value_cache_shape[1] * self.value_cache_shape[2] * self.value_cache_shape[3]
        else:
            value_cache_size = 0
        cache_bytes = self._get_cache_bytes(self.cache_dtype)
        key_need_to_allocate_bytes = args.num_cpu_blocks * cache_bytes * key_cache_size
        value_need_to_allocate_bytes = args.num_cpu_blocks * cache_bytes * value_cache_size
        if args.cache_dtype == "block_wise_fp8":
            cache_scales = paddle.empty(shape=[], dtype=paddle.get_default_dtype())
            cache_scales_size = self.key_cache_shape[1] * self.key_cache_shape[2]
            scales_key_need_to_allocate_bytes = args.num_cpu_blocks * cache_scales.element_size() * cache_scales_size
            scales_value_need_to_allocate_bytes = args.num_cpu_blocks * cache_scales.element_size() * cache_scales_size
        logger.info(
            f"[rank {self.rank}/{self.n_ranks}] ..swap space size : {(key_need_to_allocate_bytes + value_need_to_allocate_bytes) / 1024 ** 3:.2f}GB"
        )
        if args.num_cpu_blocks == 0:
            logger.info(f"[rank {self.rank}/{self.n_ranks}] 💡 no swap space (cpu cache) is specified.")
            self.swap_space_ready_signal.value[self.rank] = 1
            return
        logger.info(f"[rank {self.rank}/{self.n_ranks}] Initializing swap space (cpu cache) for all layers.")
        paddle.set_device("cpu")
        self.k_dst_ptrs = []
        self.v_dst_ptrs = []
        self.k_scales_ptrs = []
        self.v_scales_ptrs = []
        for i in range(args.num_layers + self.num_extra_layers):
            key_name = f"key_caches_{i}_rank{self.rank}"
            val_name = f"value_caches_{i}_rank{self.rank}"
            key_cache_scales_name = f"key_cache_scales_{i}_rank{self.rank}"
            value_cache_scales_name = f"value_cache_scales_{i}_rank{self.rank}"
            logger.info(
                f"[rank {self.rank}/{self.n_ranks}] ..creating cpu cache for layer {i}: {(key_need_to_allocate_bytes + value_need_to_allocate_bytes) / 1024 ** 3:.2f}GB"
            )
            self.cpu_cache_kvs[key_name] = cuda_host_alloc(key_need_to_allocate_bytes)
            self.k_dst_ptrs.append(self.cpu_cache_kvs[key_name])
            if args.cache_dtype == "block_wise_fp8":
                self.cpu_cache_kvs[key_cache_scales_name] = cuda_host_alloc(scales_key_need_to_allocate_bytes)
                self.k_scales_ptrs.append(self.cpu_cache_kvs[key_cache_scales_name])
            if value_need_to_allocate_bytes > 0:
                self.cpu_cache_kvs[val_name] = cuda_host_alloc(value_need_to_allocate_bytes)
                self.v_dst_ptrs.append(self.cpu_cache_kvs[val_name])
                if args.cache_dtype == "block_wise_fp8":
                    self.cpu_cache_kvs[value_cache_scales_name] = cuda_host_alloc(scales_value_need_to_allocate_bytes)
                    self.v_scales_ptrs.append(self.cpu_cache_kvs[value_cache_scales_name])
        logger.info(f"[rank {self.rank}/{self.n_ranks}] ✅ swap space (cpu cache) is ready!")
        self.swap_space_ready_signal.value[self.rank] = 1

    def _get_cache_bytes(self, cache_dtype):
        if cache_dtype == "bfloat16":
            cache_bytes = 2
        elif cache_dtype in ["uint8", "block_wise_fp8"]:
            cache_bytes = 1
        else:
            raise ValueError(f"Unsupported cache dtype: {cache_dtype}")
        return cache_bytes

    def _storage_exist_block_num(self, k_keys: List[str], v_keys: List[str]):
        """
        Given the k_keys and v_keys, get the valid blocks number that
        can be prefetched from storage backend.
        """
        assert len(k_keys) == len(v_keys), "k_keys and v_keys must have the same length."
        result = self.storage_backend.exists(k_keys + v_keys)

        # only consider the case when both key and value exist
        num = 0
        for k, v in zip(k_keys, v_keys):
            if result[k] and result[v]:
                num += 1
        return num

    def _run_read_storage(self, k_cache_keys, v_cache_keys, gpu_block_ids, cpu_block_ids):
        try:
            logger.debug(
                f"_run_read_storage, key_hash_keys: {k_cache_keys}, "
                f"value_hash_keys: {v_cache_keys}, gpu_block_ids: {gpu_block_ids}"
            )

            block_num = len(gpu_block_ids)
            keys = k_cache_keys + v_cache_keys
            k_cache_ptrs = [self.storage_key_read_buffer + i * self.storage_buffer_stride_bytes for i in cpu_block_ids]
            v_cache_ptrs = [
                self.storage_value_read_buffer + i * self.storage_buffer_stride_bytes for i in cpu_block_ids
            ]
            kv_cache_ptrs = k_cache_ptrs + v_cache_ptrs
            kv_block_sizes = [self.storage_buffer_stride_bytes] * block_num * 2  # key and value
            result = self.storage_backend.batch_get(keys, target_locations=kv_cache_ptrs, target_sizes=kv_block_sizes)

            k_result, v_result = result[:block_num], result[block_num:]
            success_block_num = 0
            for k, v in zip(k_result, v_result):
                if k > 0 and v > 0:
                    success_block_num += 1
            logger.debug(f"_run_read_storage, success_block_num: {success_block_num}")
            valid_gpu_block_ids = gpu_block_ids[:success_block_num]
            valid_cpu_block_ids = cpu_block_ids[:success_block_num]

            mode = 1  # cpu ==> gpu
            swap_cache_layout(
                self.gpu_cache_k_tensors,
                self.storage_key_read_buffer,
                self.key_cache_shape,
                valid_gpu_block_ids,
                valid_cpu_block_ids,
                self.device,
                mode,
            )
            swap_cache_layout(
                self.gpu_cache_v_tensors,
                self.storage_value_read_buffer,
                self.value_cache_shape,
                valid_gpu_block_ids,
                valid_cpu_block_ids,
                self.device,
                mode,
            )

            return valid_gpu_block_ids
        except Exception as e:
            logger.error(
                f"[rank {self.rank}/{self.n_ranks}] An error occurred in _run_read_storage: "
                f"error:{e}, {traceback.format_exc()}"
            )
            raise

    def read_storage_task(self, task_id, keys, gpu_block_ids, timeout=0.1):
        """Read cache from the storage backend to the GPU memory."""
        try:
            logger.debug(
                f"read_storage_task, task id: {task_id}, hash_keys: {keys}, "
                f"gpu_block_ids: {gpu_block_ids}, timeout: {timeout}"
            )
            k_cache_keys = [f"{key}_key_{self.rank}" for key in keys]
            v_cache_keys = [f"{key}_value_{self.rank}" for key in keys]
            match_block_num = self._storage_exist_block_num(k_cache_keys, v_cache_keys)
            logger.debug(f"read_storage_task, match {match_block_num} blocks from storage for task id: {task_id}")

            k_cache_keys = k_cache_keys[:match_block_num]
            v_cache_keys = v_cache_keys[:match_block_num]
            gpu_block_ids = gpu_block_ids[:match_block_num]
            cpu_block_ids = [i for i in range(match_block_num)]
            valid_gpu_block_ids = []

            if match_block_num > 0:
                # TODO: support timeout with actual block count
                try:
                    valid_gpu_block_ids = self._run_read_storage(
                        k_cache_keys, v_cache_keys, gpu_block_ids, cpu_block_ids
                    )
                    logger.info(
                        f"read_storage_task, finish loading {match_block_num} blocks from storage for task {task_id}."
                    )
                except Exception as e:
                    logger.error(f"[rank {self.rank}/{self.n_ranks}] An error occurred: {task_id} {e}")
                    valid_gpu_block_ids = []

            result = (CacheStatus.STORAGE2GPU, task_id, keys, valid_gpu_block_ids)
            self.cache_task_queue.swap_storage_to_gpu_barrier.wait()
            self.cache_task_queue.swap_storage_to_gpu_barrier.reset()
            self.cache_task_queue.put_transfer_done_signal(result)
            logger.debug(f"read_storage_task: put_transfer_done_signal {result}")
            logger.info(
                f"read_storage_task: put_transfer_done_signal for transfer_task_id {task_id}, "
                f"valid block num {len(valid_gpu_block_ids)}"
            )
        except Exception as e:
            logger.error(
                f"[rank {self.rank}/{self.n_ranks}] An error occurred in read_storage_task: "
                f"task_id: {task_id}, error:{e}, {traceback.format_exc()}"
            )

    def _run_write_back_storage(self, k_cache_keys, v_cache_keys, gpu_block_ids, cpu_block_ids):
        try:
            logger.debug(
                f"_run_write_back_storage, k_cache_keys: {k_cache_keys}, v_cache_keys: {v_cache_keys}, "
                f"gpu_block_ids: {gpu_block_ids}"
            )
            key_cache_size = [
                self.key_cache_shape[0],
                self.key_cache_shape[1],
                self.key_cache_shape[2],
                self.key_cache_shape[3],
            ]
            mode = 0  # gpu ==> cpu
            swap_cache_layout(
                self.gpu_cache_k_tensors,
                self.storage_key_write_buffer,
                key_cache_size,
                gpu_block_ids,
                cpu_block_ids,
                self.device,
                mode,
            )
            swap_cache_layout(
                self.gpu_cache_v_tensors,
                self.storage_value_write_buffer,
                key_cache_size,
                gpu_block_ids,
                cpu_block_ids,
                self.device,
                mode,
            )

            block_num = len(gpu_block_ids)
            keys = k_cache_keys + v_cache_keys
            k_cache_ptrs = [
                self.storage_key_write_buffer + i * self.storage_buffer_stride_bytes for i in cpu_block_ids
            ]
            v_cache_ptrs = [
                self.storage_value_write_buffer + i * self.storage_buffer_stride_bytes for i in cpu_block_ids
            ]
            kv_cache_ptrs = k_cache_ptrs + v_cache_ptrs
            kv_block_sizes = [self.storage_buffer_stride_bytes] * block_num * 2  # key and value
            self.storage_backend.batch_set(keys, target_locations=kv_cache_ptrs, target_sizes=kv_block_sizes)
        except Exception as e:
            logger.error(
                f"[rank {self.rank}/{self.n_ranks}] An error occurred in _run_write_back_storage: "
                f"error:{e}, {traceback.format_exc()}"
            )

    def write_back_storage_task(self, task_id, keys, gpu_block_ids, timeout=0.1):
        """
        Write cache to the storage backend from the GPU memory.
        """
        try:
            logger.debug(
                f"write cache to storage, keys: {keys}, gpu_block_ids: {gpu_block_ids}, "
                f"task_id: {task_id}, timeout: {timeout}"
            )

            k_cache_keys = [f"{key}_key_{self.rank}" for key in keys]
            v_cache_keys = [f"{key}_value_{self.rank}" for key in keys]
            match_block_num = self._storage_exist_block_num(k_cache_keys, v_cache_keys)

            k_cache_keys = k_cache_keys[match_block_num:]
            v_cache_keys = v_cache_keys[match_block_num:]
            gpu_block_ids = gpu_block_ids[match_block_num:]
            cpu_block_ids = [i for i in range(len(gpu_block_ids))]

            if len(k_cache_keys) == 0:
                logger.info(f"No uncached keys found for task {task_id}")
                gpu_block_ids = []
            else:
                try:
                    # TODO: support timeout with actual block count
                    self._run_write_back_storage(k_cache_keys, v_cache_keys, gpu_block_ids, cpu_block_ids)
                except Exception as e:
                    logger.error(f"Error in write back storage task: {e}")
                    gpu_block_ids = []

            result = (CacheStatus.GPU2STORAGE, task_id, keys, gpu_block_ids)
            self.cache_task_queue.swap_to_storage_barrier.wait()
            if self.rank == 0:  # 只有当rank为0时执行同步操作
                self.cache_task_queue.swap_to_storage_barrier.reset()
                self.cache_task_queue.put_transfer_done_signal(result)  # 发送传输完成信号
                logger.debug(f"write_back_storage_task: put_transfer_done_signal {result}")
                logger.info(f"write_back_storage_task: put_transfer_done_signal for transfer_task_id {task_id}")
        except Exception as e:
            logger.error(
                f"[rank {self.rank}/{self.n_ranks}] An error occurred in write_back_storage_task: "
                f"error:{e}, {traceback.format_exc()}"
            )

    def _do_swap_to_cpu_task(
        self,
        swap_node_ids,
        gpu_block_id,
        cpu_block_id,
        event_type,
        transfer_task_id,
    ):
        """
        swap cache GPU->CPU
        """
        self.cache_task_queue.swap_to_cpu_barrier1.wait()
        if self.rank == 0:
            self.cache_task_queue.swap_to_cpu_barrier1.reset()
        result = self._transfer_data(
            swap_node_ids,
            gpu_block_id,
            cpu_block_id,
            event_type,
            transfer_task_id,
        )
        self.cache_task_queue.swap_to_cpu_barrier2.wait()
        if self.rank == 0:
            self.cache_task_queue.swap_to_cpu_barrier2.reset()
            self.cache_task_queue.put_transfer_done_signal(result)
            logger.debug(f"_do_swap_to_cpu_task: put_transfer_done_signal {result}")
            logger.info(f"_do_swap_to_cpu_task: put_transfer_done_signal for transfer_task_id {transfer_task_id}")

    def _do_swap_to_gpu_task(
        self,
        swap_node_ids,
        gpu_block_id,
        cpu_block_id,
        event_type,
        transfer_task_id,
    ):
        """
        swap cache CPU->GPU
        """
        self.cache_task_queue.swap_to_gpu_barrier1.wait()
        if self.rank == 0:
            self.cache_task_queue.swap_to_gpu_barrier1.reset()
        result = self._transfer_data(
            swap_node_ids,
            gpu_block_id,
            cpu_block_id,
            event_type,
            transfer_task_id,
        )
        self.cache_task_queue.swap_to_gpu_barrier2.wait()
        if self.rank == 0:
            self.cache_task_queue.swap_to_gpu_barrier2.reset()
            self.cache_task_queue.put_transfer_done_signal(result)
            logger.debug(f"_do_swap_to_gpu_task: put_transfer_done_signal {result}")
            logger.info(f"_do_swap_to_gpu_task: put_transfer_done_signal for transfer_task_id {transfer_task_id}")

    def check_work_status(self, time_interval_threashold=envs.FD_CACHE_PROC_EXIT_TIMEOUT):
        """
        Check the health of the model server by checking whether all workers are alive.

        """
        if self.worker_healthy_live_signal.value[0]:
            elapsed_time = time.time() - self.worker_healthy_live_signal.value[0]
            if elapsed_time > time_interval_threashold:
                return False, "Worker Service Not Healthy"

        return True, ""

    def do_data_transfer(self):
        """
        do data transfer task
        """

        consecutive_error_count = 0
        max_errors = (
            envs.FD_CACHE_PROC_ERROR_COUNT
        )  # After this many consecutive errors, check if the worker process exists.

        while True:
            try:
                if self.rank == 0:
                    if not self.cache_task_queue.empty():
                        self.cache_task_broadcast_signal.value[0] = 1
                if self.n_ranks > 1:
                    self.cache_task_queue.barrier1.wait()
                    if self.rank == 0:
                        self.cache_task_queue.barrier1.reset()
                if self.cache_task_broadcast_signal.value[0] == 1:
                    data, read_finish = self.cache_task_queue.get_transfer_task()
                    logger.debug(f"transfer data: get_transfer_task {data}")
                    if read_finish:
                        self.cache_task_broadcast_signal.value[0] = 0
                    event_type, transfer_task_id = data[0], data[1]
                    if event_type.value == CacheStatus.SWAP2CPU.value:
                        swap_node_ids, gpu_block_id, cpu_block_id = data[2:]
                        self.swap_to_cpu_thread_pool.submit(
                            self._do_swap_to_cpu_task,
                            swap_node_ids,
                            gpu_block_id,
                            cpu_block_id,
                            event_type,
                            transfer_task_id,
                        )
                    elif event_type.value == CacheStatus.SWAP2GPU.value:
                        swap_node_ids, gpu_block_id, cpu_block_id = data[2:]
                        self.swap_to_gpu_thread_pool.submit(
                            self._do_swap_to_gpu_task,
                            swap_node_ids,
                            gpu_block_id,
                            cpu_block_id,
                            event_type,
                            transfer_task_id,
                        )
                    elif event_type.value == CacheStatus.STORAGE2GPU.value:
                        hash_keys, gpu_block_ids, timeout = data[2:]
                        self.read_storage_thread_pool.submit(
                            self.read_storage_task,
                            transfer_task_id,
                            hash_keys,
                            gpu_block_ids,
                            timeout,
                        )
                    elif event_type.value == CacheStatus.GPU2STORAGE.value:
                        hash_keys, gpu_block_ids, timeout = data[2:]
                        self.write_back_storage_thread_pool.submit(
                            self.write_back_storage_task,
                            transfer_task_id,
                            hash_keys,
                            gpu_block_ids,
                            timeout,
                        )
                else:
                    if self.n_ranks > 1:
                        self.cache_task_queue.barrier2.wait()
                        if self.rank == 0:
                            self.cache_task_queue.barrier2.reset()
                    continue

                if self.n_ranks > 1:
                    self.cache_task_queue.barrier3.wait()
                    if self.rank == 0:
                        self.cache_task_queue.barrier3.reset()

                consecutive_error_count = 0

            except (BrokenPipeError, EOFError, ConnectionResetError) as e:
                # When a cache_transfer_manager process remains, it keeps printing error logs and may exhaust disk space.
                # Add a check to see if the worker process is alive; if it has ended, exit the loop to stop continuous logging.
                logger.error(f"[CacheTransferManager] Connection broken: {e}")
                consecutive_error_count += 1
                if consecutive_error_count > max_errors:
                    try:
                        status, msg = self.check_work_status()
                    except Exception:
                        status = True

                    if status is False:
                        logger.critical(
                            f"The Worker process has been inactive for over {envs.FD_CACHE_PROC_EXIT_TIMEOUT} seconds, and the Cache process will automatically terminate (the waiting timeout can be extended via FD_CACHE_PROC_EXIT_TIMEOUT)."
                        )
                        break
                time.sleep(1)
                continue

            except Exception as e:
                logger.info(f"do_data_transfer: error: {e}, {str(traceback.format_exc())}")

    def _transfer_data(
        self,
        swap_node_ids,
        task_gpu_block_id,
        task_cpu_block_id,
        event_type,
        transfer_task_id,
    ):
        """
        transfer data
        task_gpu_block_id format: [[block_id0, [fold_block_id0, fold_block_id1]],
            [block_id1, [fold_block_id0, fold_block_id1]], ...]
        """
        logger.debug(
            f"transfer data: transfer_task_id {transfer_task_id}: swap_node_ids {swap_node_ids}"
            + f"task_gpu_block_id {task_gpu_block_id} task_cpu_block_id {task_cpu_block_id} event_type {event_type}"
        )
        start_time = time.time()
        try:
            # transform block id
            assert len(task_gpu_block_id) == len(task_cpu_block_id)
            gpu_block_ids = task_gpu_block_id
            cpu_block_ids = task_cpu_block_id

            if event_type.value == CacheStatus.SWAP2CPU.value:
                swap_cache_all_layers(
                    self.gpu_cache_k_tensors,
                    self.k_dst_ptrs,
                    self.num_cpu_blocks,
                    gpu_block_ids,
                    cpu_block_ids,
                    self.device,
                    0,
                )
                swap_cache_all_layers(
                    self.gpu_cache_v_tensors,
                    self.v_dst_ptrs,
                    self.num_cpu_blocks,
                    gpu_block_ids,
                    cpu_block_ids,
                    self.device,
                    0,
                )
                if self.cache_dtype == "block_wise_fp8":
                    swap_cache_all_layers(
                        self.gpu_cache_scales_k_tensors,
                        self.k_scales_ptrs,
                        self.num_cpu_blocks,
                        gpu_block_ids,
                        cpu_block_ids,
                        self.device,
                        0,
                    )
                    swap_cache_all_layers(
                        self.gpu_cache_scales_v_tensors,
                        self.v_scales_ptrs,
                        self.num_cpu_blocks,
                        gpu_block_ids,
                        cpu_block_ids,
                        self.device,
                        0,
                    )

            elif event_type.value == CacheStatus.SWAP2GPU.value:
                swap_cache_all_layers(
                    self.gpu_cache_k_tensors,
                    self.k_dst_ptrs,
                    self.num_cpu_blocks,
                    gpu_block_ids,
                    cpu_block_ids,
                    self.device,
                    1,
                )
                swap_cache_all_layers(
                    self.gpu_cache_v_tensors,
                    self.v_dst_ptrs,
                    self.num_cpu_blocks,
                    gpu_block_ids,
                    cpu_block_ids,
                    self.device,
                    1,
                )
                if self.cache_dtype == "block_wise_fp8":
                    swap_cache_all_layers(
                        self.gpu_cache_scales_k_tensors,
                        self.k_scales_ptrs,
                        self.num_cpu_blocks,
                        gpu_block_ids,
                        cpu_block_ids,
                        self.device,
                        1,
                    )
                    swap_cache_all_layers(
                        self.gpu_cache_scales_v_tensors,
                        self.v_scales_ptrs,
                        self.num_cpu_blocks,
                        gpu_block_ids,
                        cpu_block_ids,
                        self.device,
                        1,
                    )
            else:
                logger.warning(
                    f"transfer data: Get unexpected event type {event_type}, only SWAP2CPU and SWAP2GPU supported"
                )
        except Exception as e:
            logger.error(f"transfer data: error: {e}")
            raise e
        end_time = time.time()
        elasped_time = end_time - start_time
        logger.info(
            f"transfer data: transfer_task_id {transfer_task_id} event_type {event_type}: "
            + f"transfer {len(gpu_block_ids)} blocks done  elapsed_time {elasped_time:.4f}"
        )
        return (
            event_type,
            transfer_task_id,
            swap_node_ids,
            task_gpu_block_id,
            task_cpu_block_id,
        )

    def clear_or_update_caches(self, args):
        # TODO XPU support RL
        if unset_data_ipc is None:
            return
        logger.info("Start a thread to clear/restore kv cache when model weights are cleared/updated.")
        logger.info(f"FD_ENABLE_SWAP_SPACE_CLEARING={envs.FD_ENABLE_SWAP_SPACE_CLEARING}")
        kv_cache_status = np.zeros([1], dtype=np.int32)
        kv_cache_status_signal = IPCSignal(
            name="kv_cache_status",
            array=kv_cache_status,
            dtype=np.int32,
            suffix=self.ipc_suffix,
            create=False,
        )
        while True:
            if kv_cache_status_signal.value[0] == KVCacheStatus.CLEARING:
                assert args.splitwise_role == "mixed", "Only mixed mode supports clearing cache."
                try:
                    logger.info(
                        f"[rank {self.rank}/{self.n_ranks}] Start clearing caches {self.cache_ready_signal.value}"
                    )
                    # clear cpu caches
                    if envs.FD_ENABLE_SWAP_SPACE_CLEARING:
                        paddle.set_device("cpu")
                        for ptrs in self.k_dst_ptrs + self.v_dst_ptrs:
                            cuda_host_free(ptrs)
                        self.cpu_cache_kvs.clear()
                        self.k_dst_ptrs.clear()
                        self.v_dst_ptrs.clear()
                        gc.collect()
                        # reset swap_space_ready_signal
                        self.swap_space_ready_signal.value[self.rank] = 0
                        while np.sum(self.swap_space_ready_signal.value) != 0:
                            time.sleep(0.1)

                    # clear gpu caches
                    set_device(self.device)
                    for name, tensor in self.gpu_cache_kvs.items():
                        unset_data_ipc(tensor, name, True, False)
                    self.gpu_cache_kvs.clear()
                    self.gpu_cache_k_tensors.clear()
                    self.gpu_cache_v_tensors.clear()

                    # reset cache_ready_signal
                    self.cache_ready_signal.value[self.rank] = 0
                    logger.info(
                        f"[rank {self.rank}/{self.n_ranks}] Finish clearing caches {self.cache_ready_signal.value}"
                    )

                    # wait for all ranks caches to be cleared
                    if np.sum(self.cache_ready_signal.value) != 0:
                        time.sleep(0.1)

                    # reset kv_cache_status_signal
                    kv_cache_status_signal.value[0] = KVCacheStatus.CLEARED
                    logger.info("All ranks finish clearing caches")

                except Exception as e:
                    logger.error(f"[rank {self.rank}/{self.n_ranks}] Failed to clear caches: {e}")

            elif kv_cache_status_signal.value[0] == KVCacheStatus.UPDATING:
                assert args.splitwise_role == "mixed", "Only mixed mode supports updating cache."
                try:
                    logger.info(
                        f"[rank {self.rank}/{self.n_ranks}] Start restoring caches {self.cache_ready_signal.value}"
                    )
                    # restore cpu cache
                    if envs.FD_ENABLE_SWAP_SPACE_CLEARING:
                        self._init_cpu_cache(args)
                        while np.sum(self.swap_space_ready_signal.value) != args.mp_num:
                            time.sleep(0.1)

                    # restore gpu cache and set cache_ready_signal
                    self._init_gpu_cache(args)
                    logger.info(
                        f"[rank {self.rank}/{self.n_ranks}] Finish restoring caches {self.cache_ready_signal.value}"
                    )

                    # wait for all ranks caches to be ready
                    while np.sum(self.cache_ready_signal.value) != args.mp_num:
                        time.sleep(0.1)

                    # set kv_cache_status_signal
                    logger.info("All ranks finish restoring caches")
                    kv_cache_status_signal.value[0] = KVCacheStatus.NORMAL

                except Exception as e:
                    logger.error(f"[rank {self.rank}/{self.n_ranks}] Failed to restore caches: {e}")

            time.sleep(0.1)


def main():
    """
    启动cache manager
    """
    cache_manager = CacheTransferManager(args)
    cache_manager.do_data_transfer()


if __name__ == "__main__":

    args = parse_args()
    rank_id = args.rank + args.local_data_parallel_id * args.mp_num
    logger = get_logger("cache_transfer_manager", f"cache_transfer_manager_tprank{args.rank}.log")
    logger.info(f"args: {vars(args)}")
    set_device(args.device_id)
    try:
        main()
    except Exception as e:
        logger.error(f"cache_transfer_manager failed with error: {e}, traceback: {traceback.format_exc()}")
        raise
